High-dimensional Two-sample Precision Matrices Test: An Adaptive Approach through Multiplier Bootstrap

TL;DR

This paper introduces a data-adaptive test for comparing two high-dimensional precision matrices, effective against both sparse and dense differences, using a multiplier bootstrap approach with proven theoretical properties.

Contribution

It proposes a novel adaptive testing method for high-dimensional precision matrices that outperforms existing tests limited to sparse alternatives.

Findings

Test performs well under various alternative scenarios

Theoretical properties including asymptotic size and power are established

Applied successfully to gene expression data related to lung cancer

Abstract

Precision matrix, which is the inverse of covariance matrix, plays an important role in statistics, as it captures the partial correlation between variables. Testing the equality of two precision matrices in high dimensional setting is a very challenging but meaningful problem, especially in the differential network modelling. To our best knowledge, existing test is only powerful for sparse alternative patterns where two precision matrices differ in a small number of elements. In this paper we propose a data-adaptive test which is powerful against either dense or sparse alternatives. Multiplier bootstrap approach is utilized to approximate the limiting distribution of the test statistic. Theoretical properties including asymptotic size and power of the test are investigated. Simulation study verifies that the data-adaptive test performs well under various alternative scenarios. The practical usefulness of the test is illustrated by applying it to a gene expression data set associated with lung cancer.